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Using machine learning to identify the best CRISPR-Cas9 targets for functional gene knockout
EP27256
Poster Title: Using machine learning to identify the best CRISPR-Cas9 targets for functional gene knockout
Submitted on 09 Feb 2018
Author(s): Jesse Stombaugh, Shawn McClelland, Emily M. Anderson, Žaklina Strezoska, Elena Maksimova, Annaleen Vermeulen, Steve Lenger, Tyler Reed, and Anja van Brabant Smith
Affiliations: Dharmacon part of Horizon Discovery Group
This poster was presented at Keystone 2018
Poster Views: 913
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Poster Information
Abstract: We systematically transfected >1100 synthetic crRNA:tracrRNA targeting components of the proteasome into a reporter cell line in which knockout of proteasome function results in fluorescence of a ubiquitin-EGFP fusion protein that is normally degraded by the proteasome pathway. Using the results from the functional assay, we developed and trained a machine-learning algorithm to score crRNAs based on how likely they were to produce functional knockout of targeted genes (functionality score). To minimize potential off-targets, we developed a rigorous specificity tool that is able to detect and score mismatches as well as gapped alignments that are typically missed using most existing specificity tools (specificity score). We combined this comprehensive specificity check with our functionality algorithm to select and score highly specific and functional crRNAs for any given gene target and also generated a whole-genome arrayed crRNA library for screening applications. Summary: Although the literature shows most CRISPR-Cas9 guide RNAs will produce significant indel formation, not all guide RNAs produce a functional gene knockout, which is the desired result for the majority of these experiments. To better understand the parameters affecting the efficiency for a functional gene knockout, we utilized synthetic crRNA and tracrRNA, which can be chemically synthesized rapidly without the need for cloning and sequencing. Report abuse »
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